Trehalose synthesis during differentiation in Dictyostelium discoideum. I. Analysis and predictions by computer simulation.
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Cell Differentiation↗
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[Methods of predicting the computed approximate thresholds of reflex action and the maximal one-time permissible concentration limits for organic substances in the atmosphere].
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Air Pollutants↗
[The study of neuropsychological alterations following coronary artery bypass operation as predicted by computed tomography scan of the brain].
The objective of this clinical study is to provide information regarding the association between coexistent cerebrovascular disease and neuropsychological abnormalities after coronary artery bypass operations. Computed tomography scan of the brain was performed in 104 patients pre-operatively, and their post-operative neuropsychological functions were evaluated. The patients were categorized as follows according to the CT findings: Seventy-three patients showed normal or slight cerebral cortical atrophy which usually seen in patients over fifty of age (group A). Sixteen showed moderate or severe cortical atrophy (group B). Fifteen patients demonstrated the characteristic findings of Binswanger type; severe white matter hypodensity especially in frontal horns and dilated ventricles (group C). Overt neuropsychological dysfunction was not observed in patient in groups A and B. Six patients in group C showed a combination of dementia, bizarre behavior, disorientation and gait dyspraxia following bypass operations. The pseudobulbar signs were also found in 3 patients. These clinical abnormalities persisted for six days to three weeks, and were most often reversible. Although the underlying mechanism of these deleterious alterations is not elucidated, the ischemic nature of the characteristic white matter lesions was highly suspected. The arteriosclerotic changes of the arteriole of the cerebral cortex and hypoperfusion during cardio-pulmonary bypass were supposed to be responsible. Therefore it was concluded that special attention should be focused on neurological evaluation for bypass surgery in group C patients.
Adult↗
Methods for predicting bacterial protein subcellular localization.
The computational prediction of the subcellular localization of bacterial proteins is an important step in genome annotation and in the search for novel vaccine or drug targets. Since the 1991 release of PSORT I--the first comprehensive algorithm to predict bacterial protein localization--many other localization prediction tools have been developed. These methods offer significant improvements in predictive performance over PSORT I and the accuracy of some methods now rivals that of certain high-throughput laboratory methods for protein localization identification.
Amino Acid Sequence↗
Modeling the Dornier HM3 lithotripter.
The computational modeling of a Dornier HM3 electrohydraulic, extracorporeal shock wave lithotripter is considered. In order to produce large amplitude shock waves for the purpose of pulverizing renal and ureteric calculi (stones), the HM3 uses a hemi-ellipsoidal bowl to focus the spherical field generated by a high-voltage spark gap. The initial propagation of the bowl-focused field is accomplished using a recently developed nonplanar source algorithm. An updated version of an existing nonlinear acoustic beam propagation model is then used to consider the subsequent propagation of the field. The resulting modeling sequence accounts for the effects of diffraction, attenuation, dispersion, nonlinearity, and (planar) reflection and refraction. The water path computed predictions agree well with existing measurements. The computed in vivo predictions suggest that the Dornier HM3's clinical performance is not significantly different than its water path performance.
Equipment Design↗
Use of real time leukaemia data to validate model predictions based on analyses and computer simulations.
Predictions arising out of a mathematical model that describes the expansion of leukaemia from a diffusion-orientated perspective are critiqued and validated by employing available real time data. Based on agreements found between model predictions and the data, but mindful of the limitations it presents, it is concluded that the model could be used to describe the dynamics of normal and abnormal cells in leukaemia. It is suggested that further studies of the behaviour of certain normal cell types in contrast to abnormal cells during leukaemic development could engender additional insights into leukaemia and its treatment.
Cell Death↗
Modeling regulatory networks with weight matrices.
Systematic gene expression analyses provide comprehensive information about the transcriptional response to different environmental and developmental conditions. With enough gene expression data points, computational biologists may eventually generate predictive computer models of transcription regulation. Such models will require computational methodologies consistent with the behavior of known biological systems that remain tractable. We represent regulatory relationships between genes as linear coefficients or weights, with the "net" regulation influence on a gene's expression being the mathematical summation of the independent regulatory inputs. Test regulatory networks generated with this approach display stable and cyclically stable gene expression levels, consistent with known biological systems. We include variables to model the effect of environmental conditions on transcription regulation and observed various alterations in gene expression patterns in response to environmental input. Finally, we use a derivation of this model system to predict the regulatory network from simulated input/output data sets and find that it accurately predicts all components of the model, even with noisy expression data.
Computational Biology↗
Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures.
MOTIVATION: Since the whole genome sequences for many species are currently available, computational predictions of RNA secondary structures and computational identifications of those non-coding RNA regions by comparative genomics become important, and require more advanced alignment methods. Recently, an approach of structural alignments for RNA sequences has been introduced to solve these problems. By structural alignments, we mean a pairwise alignment to align an unfolded RNA sequence into a folded RNA sequence of known secondary structure. Pair HMMs on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignments of RNA secondary structures, but are incapable of handling pseudoknots. On the other hand, tree adjoining grammars (TAGs) is a subclass of context-sensitive grammar, which is suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots. RESULTS: We propose the pair stochastic tree adjoining grammars (PSTAGs) for modeling RNA secondary structures including pseudoknots and show the strong experimental evidences that modeling pseudoknot structures significantly improves the prediction accuracies of RNA secondary structures. First, we extend the notion of PHMMTSs defined on alignments of 'trees' to PSTAGs defined on alignments of "TAG (derivation) trees", which represent a top-down parsing process of TAGs and are functionally equivalent to derived trees of TAGs. Second, we modify PSTAGs so that it takes as input a pair of a linear sequence and a TAG tree representing a pseudoknot structure of RNA to produce a structural alignment. Then, we develop a polynomial-time algorithm for obtaining an optimal structural alignment by PSTAGs, based on dynamic programming parser. We have done several computational experiments for predicting pseudoknots by PSTAGs, and our computational experiments suggests that prediction of RNA pseudoknot structures by our method are more efficient and biologically plausible than by other conventional methods. The binary code for PSTAG method is freely available from our website at http://www.dna.bio.keio.ac.jp/pstag/.
Algorithms↗
The induction of rules for predicting chemical carcinogenesis in rodents.
This paper presents results from an ongoing effort in applying a variety of induction-based methods to the problem of predicting the biological activity of noncongeneric (structurally dissimilar) chemicals. It describes initial experiments, the long-term goal of which is to assist toxicologists, cancer researchers, regulators, and others to predict the toxic effects of chemical compounds. We describe a series of experiments in tree and rule induction from a set of example chemicals whose carcinogenicity has been determined from long-term animal studies, and compare the resulting classification accuracy with eight published human and computer predictions for a common set of 44 test chemicals. The accuracy of our system is comparable to the most accurate human expert prediction yet published, and exceeds that of any of the computer-based predictions in the literature. The induced rules provide confirmation of current expert heuristic knowledge in this domain. These early results show that an inductive approach has excellent potential in predictive toxicology.
Animals↗
Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.
A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).
ATP Binding Cassette Transporter, Subfamily B, Mem↗
Signal peptide prediction based on analysis of experimentally verified cleavage sites.
A number of computational tools are available for detecting signal peptides, but their abilities to locate the signal peptide cleavage sites vary significantly and are often less than satisfactory. We characterized a set of 270 secreted recombinant human proteins by automated Edman analysis and used the verified cleavage sites to evaluate the success rate of a number of computational prediction programs. An examination of the frequency of amino acid in the N-terminal region of the data set showed a preference of proline and glutamine but a bias against tyrosine. The data set was compared to the SWISS-PROT database and revealed a high percentage of discrepancies with cleavage site annotations that were computationally generated. The best program for predicting signal sequences was found to be SignalP 2.0-NN with an accuracy of 78.1% for cleavage site recognition. The new data set can be utilized for refining prediction algorithms, and we have built an improved version of profile hidden Markov model for signal peptides based on the new data.
Amino Acid Motifs↗
Computer-assisted spine surgery.
Computer assistance has been shown to improve significantly the accuracy and safety of pedicle screw insertion under clinical conditions. The technique of image-guided navigation is described in this article, based on the authors' clinical experience of over 4 years. The value of navigation systems for preoperative planning is discussed. Clinical results of the application of this new method in the cervical, thoracic, and lumbosacral spine as well as the iliosacral joints are presented by means of the authors' own studies and reports from the literature. Pros and cons of computer guidance are discussed. The authors predict computer navigation will be used in percutaneous and minimally invasive procedures in the near future.
Clinical Trials as Topic↗
Conformational study of the structure of 12-crown-4-alkali metal cation complexes.
A conformational search was performed for the 12-crown-4 (12c4)-alkali metal cation complexes using two different methods, one of them is the CONFLEX method, whereby eight conformations were predicted. Computations were performed for the eight predicted conformations at the HF/6-31+G*, MP2/6-31+G*//HF/6-31+G*, B3LYP/6-31+G*, MP2/6-31+G*//B3LYP/6-31+G*, and MP2/6-31+G* levels. The calculated energies predict a C4 conformation for the 12c4-Na+, -K+, -Rb+, and -Cs+ complexes and a C(s) conformation for the 12c4-Li+ complex to be the lowest energy conformations. For most of the conformations considered, the relative energies, with respect to the C4 conformation, at the MP2/6-31+G*//B3LYP/6-31+G* are overestimated, compared to those at the MP2/6-31+G* level, the highest level of theory considerd in this report, by 0.2 kcal/mol. Larger relative energy differences are attributed to larger differences between the B3LYP and MP2 optimized geomtries. Binding enthalpies (BEs) were calculated at the above-mentioned levels for the eight conformations. The agreement between the calculated and experimental BEs is discussed.
Journal Article↗
[The search for new antiulcer agents using a computer system for predicting the antiulcer activity].
Specialised system for computer assisted prediction of antiulcer activity of chemical compounds on the basis of their structural formulae is described. Predicted activity spectrum includes antiulcer activity, antisecretory and gastroprotective effects; H2- and M1-receptors blockade, H+, K(+)-ATPase inhibition as the mechanisms of antisecretory effect. It is shown that the average prediction accuracy is 83% in leave-one-out cross validation and 82% for the independent test set. Prediction made for about 300 new chemical compounds provides the selection of 20 potential antiulcer agents, 9 of them were synthesised and tested, 5 compounds have potent antiulcer activity. The discovered antiulcer agents are classified as New Chemical Entry because antiulcer and close effects have not been earlier for any compound of this chemical series.
Anti-Ulcer Agents↗
Challenges of crystal structure prediction of diastereomeric salt pairs.
A methodology for the computational prediction of the crystal structures and resolution efficiency for diastereomeric salt pairs is developed by considering the polymorphic system of the diastereomeric salt pair (R)-1-phenylethylammonium (R/S)-2-phenylpropanoate. To alleviate the mathematical complexity of the search for minima in the lattice energy due to the presence of two flexible entities in the asymmetric unit, the range of rigid-body lattice energy global optimizations was guided by a statistical analysis of the Cambridge Structural Database for common ion-pair geometries and ion conformations. A distributed multipole model for the dominant electrostatic interactions and high-level ab initio calculations for the intramolecular energy penalty for conformational distortions are used to quantify the relative stabilities of the p- and n-salt forms. While the ab initio prediction of the known structure of the p-salt as the most stable structure was insensitive to minor changes in the rigid-ion conformations considered, the relative stabilities of the known polymorphs and hypothetical structures of the n-salt were very sensitive. Although this paper provides a significant advance over traditional search algorithms and empirical force fields in determining the structures and relative stabilities of diastereomeric salt pairs, the sensitivity of the computed lattice energies to the fine details of the ion conformations overtaxes current computational models and renders the design of diastereomeric resolution processes by computational chemistry a challenging problem.
Algorithms↗
Pedigree information to predict genetic merit for type of Holstein bulls.
Three methods of computing predicted differences for type were compared for accuracy of pedigree evaluation of young Holstein bulls. Computing methods were herdmate comparison, herdmate comparison adjusted for genetic merit of herdmates, and best linear unbiased prediction. Evaluations by progeny test were available for 1298, 1293, and 1088 sons and sires for herdmate comparison, herdmate comparison adjusted for cow index of herdmates, and best linear unbiased prediction with fewer sons having more complete pedigrees. Ten regression models were used to predict eventual progeny test evaluations of sons from combinations of evaluations for sires, dams, and maternal grandsires. Negative intercepts and smaller than expected regression coefficients suggested bias in evaluations by all methods but much less bias with best linear unbiased prediction than with other methods. Multiple correlations generally were largest for equations using evaluations by best linear unbiased prediction.
Animals↗
Improved predictions of secondary structures for RNA.
The accuracy of computer predictions of RNA secondary structure from sequence data and free energy parameters has been increased to roughly 70%. Performance is judged by comparison with structures known from phylogenetic analysis. The algorithm also generates suboptimal structures. On average, the best structure within 10% of the lowest free energy contains roughly 90% of phylogenetically known helixes. The algorithm does not include tertiary interactions or pseudoknots and employs a crude model for single-stranded regions. The only favorable interactions are base pairing and stacking of terminal unpaired nucleotides at the ends of helixes. The excellent performance is consistent with these interactions being the primary interactions determining RNA secondary structure.
Base Composition↗
Deleterious mutation prediction in the secondary structure of RNAs.
Methods for computationally predicting deleterious mutations have recently been investigated for proteins, mainly by probabilistic estimations in the context of genomic research for identifying single nucleotide polymorphisms that can potentially affect protein function. It has been demonstrated that in cases where a few homologs are available, ab initio predicted structures modeled by the Rosetta method can become useful for including structural information to improve the deleterious mutation prediction methods for proteins. In the field of RNAs where very few homologs are available at present, this analogy can serve as a precursor to investigate a deleterious mutation prediction approach that is based on RNA secondary structure. When attempting to develop models for the prediction of deleterious mutations in RNAs, useful structural information is available from folding algorithms that predict the secondary structure of RNAs, based on energy minimization. Detecting mutations with desired structural effects among all possible point mutations may then be valuable for the prediction of deleterious mutations that can be tested experimentally. Here, a method is introduced for the prediction of deleterious mutations in the secondary structure of RNAs. The mutation prediction method, based on subdivision of the initial structure into smaller substructures and construction of eigenvalue tables, is independent of the folding algorithms but relies on their success to predict the folding of small RNA structures. Application of this method to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs, is given for prokaryotic transcription termination in the thiamin pyrophosphate and S-adenosyl-methionine induced riboswitches. Ribo switches are mRNA structures that have recently been found to regulate transcription termination or translation initiation in bacteria by conformation rearrangement in response to direct metabolite binding. Predicting deleterious mutations on riboswitches may succeed to systematically intervene in bacterial genetic control.
Algorithms↗